High performance wireless networks using distributed control

ABSTRACT

A design and proof of concept of a new type of WLAN, complete with simulation and results from the simulation has been described. Each AP Node is implemented as a self-contained embedded OS unit, with all algorithms resident in its Operating system. The normal day-to-day functioning of the AP node is based entirely on resident control algorithms. Upgrades are possible through a simple secure communications interface supported by the OS kernel for each AP node. Benefits provided by a wireless network, as proposed in this invention, are that: it installs out of the box; the network is self-configuring; the network is redundant in that mesh network formalism is supported, ensuring multiple paths; load balancing is supported; there is no single point of failure; allows for decentralized execution; there is a central control; it is network application aware; there is application awareness; there is automatic channel allocation to manage and curtail RF interference, maximize non interference bandwidth and enable seamless roaming between adjoining wireless sub networks (BSS) and it supports the wireless equivalent for switching—for seamless roaming requirements.

CROSS-REFERENCE

This application is a continuation of co-pending U.S. patent applicationSer. No. 10/434,948, entitled HIGH PERFORMANCE WIRELESS NETWORKS USINGDISTRIBUTED CONTROL filed May 8, 2003, which is incorporated herein byreference for all purposes, which claims priority to U.S. ProvisionalApplication No. 60/421,930, filed Oct. 28, 2002, which is hereinincorporated by reference.

FIELD OF THE INVENTION

The present invention describes an adaptive software layer for adistributed set of wireless communication devices that communicate witheach other in a wireless network. The software control layer addresseslow latency requirements (for applications such as voice) and highthroughput requirements (for applications involving data transfer). Oneembodiment of the present invention provides the software control forwireless (devices, such as, but not limited to Access Points, employedin a convergent enterprise network supporting voice, video and data. Atopical application of the software control layer is a home or personalnetworking environment (PAN) using Ultra Wide Band or Wi-Fi as thecommunications medium. Another topical application of the adaptivesoftware control layer is extending wireless communication range usingmesh networks for Metropolitan Area Networks (MAN). Lastly, the softwarecontrol layer is also relevant to both home and enterprise WirelessLocal Area Networks (WLANS).

BACKGROUND OF THE INVENTION

There is increasing demand within the enterprise, the home and withincities to employ one wireless network to support both voice, video anddata traffic. Currently, the “voice” network, e.g. the telephone system,is separate from the “data” network e.g. Internet connectivity andaccess to enterprise data over a Local Area Network (LAN). Convergenceis, as the name implies, the ability to converge these two networks intoone network, centrally managed by one access server servicing a networkof relay and leaf nodes.

The challenge lies in providing—within the same wireless network—theability to address potentially conflicting latency and throughput needsof diverse applications. For example, voice needs to be transmitted withlow delay (latency). Occasionally lost voice packets, while undesirable,is not fatal for voice transmissions. Conversely, data transmissionsmandate delivery of all packets and while low latency is desirable it isnot essential. In essence transmission across the wireless networkshould ideally be driven by the needs of the application. The tablebelow lists some types of applications and their latency requirements.

Delivery Type Description Asynchronous No constraints on delivery time(“elastic”): e.g. email Synchronous Data is time-sensitive, butrequirements flexible in terms of delays Interactive Delays noticeablebut not adversely affecting usability or functionality IsochronousTime-sensitive: delays adversely affect usability. E.g. voice/videoMission critical Data delivery delays disables functionality

A wireless network provides service to a diverse set of applications,with varied latency requirements. One approach to make dumb wirelessdevices, that are nodes of the network, more application aware byimplementing QoS (Quality of Service) reservation schemes dictated bythe application server.

Changing the rate the queue is serviced can also be accomplished byspecialized communications between wireless communication devices suchas Access Point (AP) nodes and the access server to ensure that voiceand data, for example, are serviced at different time intervals.Unfortunately, this adversely affects scalability and redundancy of thesystem: the access server is now micromanaging the network and hasbecome a single point of failure. A paramount concern of any network isdistributed control, especially a network handling voice, video anddata.

Another shortcoming of a centralized approach—central control andcentral execution—is the cost of maintaining a central control pointwith all intelligence and control at one location and dumb communicationdevices distributed in the enterprise. The cost of the central controlpoint is high, and the dumb access points are not any less expensivethan smart access points—since the smarts is in the software. Thus adistributed approach is far less expensive—. In addition to being morecost effective a distributed approach is more fault tolerant and hasbuilt in fail-safe redundancy. The only way to get redundancy out ofcentralized approaches is to buy multiple central control points—anexpensive approach.

Building a reliable wireless network comes with other constraintsspecific to wireless. Some routing paths may be best for voice andvideo, others for data. In Ethernet applications separate routing pathsis easily accomplished. But in a wireless network, operating over radio,the cost of associating and disassociating with a relay node—to switchto new routing paths—is prohibitive. Multiple radios, supportingseparate voice and data channels is possible but expensive. It ispreferable, therefore, if each AP node can support both voice and datatransmissions with a one “channel”.

SUMMARY OF THE INVENTION

Accordingly, there is a need for, and an objective of the presentinvention, to develop an adaptive wireless network, based on “smart”communication devices such as Access Points (AP) that provide embeddedintelligence at the edge of the network, are application aware andprovide cost effective distributed sensing and control of the network.An additional objective of this invention is to allow thecharacteristics of the network to be set by a centralized access server,which can thus “tune” the character of the network to be anythingbetween the two extremes of low latency to high throughput, based on theneeds of applications running in the enterprise. The invention alsosupports the possibility of running multiple types of networkssupporting anything between the two extremes of low latency and highthroughput, using multiple radios at each node for each distinct type ofnetwork.

As an illustration of central control but distributed intelligence,consider FIG. 1, where the star network depicted has low latency (SeeFIG. 1B for the latency in the status panel). However, since the signalstrength varies inversely with the distance from the Access Point, asshown in FIG. 1A in the look up table (control panel) on the top leftcorner, the price is poor throughput. If all devices are required toconnect directly to the root node as shown, regardless of their distancefrom the root node, then the overall throughput of the network can below.

Since the signal strength varies inversely with the distance, it may beadvantageous, from the perspective of better signal strength and overallbetter throughput for some AP nodes to connect to an intermediate APrather than to the root, as shown in FIG. 2C. In FIG. 2, the throughputof the network has increased, see FIG. 2B, because two nodes areconnecting to other nodes (relays) with an overall increased throughputfrom 44.1 to 62.2. (See FIGS. 1B and 2B).

The number 44.1 is a measure of the throughput computed based on thelook up table shown in FIGS. 1 and 2 where distance from the root causesa rapid decrease in throughput. The throughput is expressed as apercentage, where a connection to the root at zero distance would be themaximum possible throughput: 100%.

While the throughput increased to 62.2, the tradeoff is more hops,resulting in a loss of latency for higher throughput. In FIG. 2, thelatency has increased as a measure of the number of hops from an AccessPoint (AP) to the root node. It is 1.6, since some nodes now are 2 hopsaway from the root node.

The objective of this invention is to allow the Access Server to setsome latency/throughput constraints that causes each AP node to changetheir relationships to each other and consequently the character of thenetwork. Control parameters, set by an access server can then tune thewireless network to provide a mix between the two extremes of maxthroughput and low latency. As shown in FIG. 3, parameters set by theaccess server cause the network to self configure providing an averagelatency to 1.3 from 1.6. (See FIGS. 3B and 2B). Even though thethroughput has reduced from 62.2 to 57.0%, it is still better than 44.1,the case in FIG. 1.

The approach taken to modify the network is completely decentralized—thechanges in the network take place with algorithms running in each APnode. The Access Server does not change the characteristics of eachnode, it simply sets the parameters governing the characteristic of thenetwork—and let the AP nodes reconfigure their relationships to meet theobjectives set by the Access Server. Thus the Access Server can controlthe behavior of the network without necessarily controlling the behaviorof each node of the network. Benefits of this approach include a highlyscaleable, redundant wireless network. Some other benefits include:

-   1. Installs out of the box. No site survey or installation involved,    since system self configures-   2. Network is redundant. Mesh network formalism is supported,    ensuring multiple paths.-   3. Load balancing supported: Network nodes reroute data to avoid    load-congested nodes.-   4. No single point of failure. If a node “dies”, another optimal    routing path is selected-   5. Decentralized execution: Algorithms controlling the network nodes    resident in every node.-   6. Central control: Setting system level “tuning” parameters changes    network configuration-   7. Network application aware: latency/throughput profiles defined in    the access server-   8. Application awareness: Based on the application profile in the    access server, the network configures itself to satisfy all    application requirements as best as possible.-   9. Network is very scaleable—since execution is completely    decentralized

Uses of a self configuring application aware wireless network range fromproviding voice/data access to warehouses, factory floors,communications with process control equipment to home networkingapplications involving voice/data/video streaming. Some applicationsunder consideration include:

-   -   Monitoring process control equipment with a Publish/Subscribe        Stream server built on top of the wireless network. Examples        include chemical plants, medical devices, industrial controls.    -   Voice over IP (VOIP) based low cost communication devices for        mobility within enterprises    -   Video streaming over wireless for remote surveillance.

These and other embodiments of the present invention are further madeapparent, in the remainder of the present document, to those of ordinaryskill in the art.

BRIEF DESCRIPTION OF DRAWINGS

In order to more fully describe embodiments of the present invention,reference is made to the accompanying drawings. These drawings are notto be considered limitations in the scope of the invention, but aremerely illustrative.

FIG. 1 illustrates a wireless network in star configuration, with lowlatency but poor throughput according to an embodiment of the presentinvention; FIG. 1A illustrates the control panel; FIG. 1B illustratesthe status panel and FIG. 1C illustrates the wireless network.

FIG. 2 illustrates a wireless network with improved throughput but poorlatency according to an embodiment of the present invention; FIG. 2Aillustrates the control panel; FIG. 2B illustrates the status panel andFIG. 2C illustrates the wireless network.

FIG. 3 shows the result of how wireless network self configures to meetAccess Server requirements according to an embodiment of the presentinvention; FIG. 3A illustrates the control panel; FIG. 3B illustratesthe status panel and FIG. 3C illustrates the wireless network.

FIG. 4A indicates terminology for variables used by parent selection andload balancing algorithms according to an embodiment of the presentinvention.

FIG. 4B illustrates Routing paths selected for maximizing throughputaccording to an embodiment of the present invention.

FIG. 5 illustrates Routing paths selected for low latency according toan embodiment of the present invention.

FIG. 6 illustrates how progressively changing the latency cost factoraffects the network configuration according to an embodiment of thepresent invention.

FIG. 7 illustrates how node increases its Cost of connectivity from 0 to3 to reduce congestion according to an embodiment of the presentinvention.

FIG. 8 illustrates how progressively increasing the cost of connectivitycauses children to seek other routes according to an embodiment of thepresent invention.

FIG. 9 shows the modeling of both the DCF and PCF algorithms described,based on 802.11x specifications according to an embodiment of thepresent invention.

FIG. 10 illustrates how the worst timing constraint for an IsochronousPCF mode would be 5T according to an embodiment of the presentinvention.

FIG. 11 shows the Key distribution for AES symmetric private keydistribution under PKI for distribution according to an embodiment ofthe present invention.

FIG. 12 illustrates ACG RTOS running algorithms in Intel PXA250emulation environment according to an embodiment of the presentinvention.

FIG. 13 illustrates the process for low footprint code generation fromJava or C, with ACG RTOS library components according to an embodimentof the present invention.

FIG. 14 illustrates the implementation of a method to generate lowfootprint code from Java according to an embodiment of the presentinvention.

FIG. 15 illustrates an implementation of a complete system includingthree components of one product offering, according to an embodiment ofthe present invention.

FIG. 16 illustrates an implementation of a Dynamic Channel AllocationScheme to ensure non-interfering radio channel selection (enlargement)according to an embodiment of the present invention.

FIG. 17 illustrates an implementation of a Dynamic Channel AllocationScheme to ensure non-interfering radio channel selection according to anembodiment of the present invention.

FIG. 18 illustrates an implementation of a Dynamic Channel AllocationScheme to ensure seamless roaming in a multi hop mesh network accordingto an embodiment of the present invention.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

The description above and below and the drawings of the present documentfocus on one or more currently preferred embodiments of the presentinvention and also describe some exemplary optional features and/oralternative embodiments. The description and drawings are for thepurpose of illustration and not limitation. Those of ordinary skill inthe art would recognize variations, modifications, and alternatives.Such variations, modifications, and alternatives are also within thescope of the present invention. Section titles are terse and are forconvenience only.

The object of this invention is a new type of wireless AP nodes that:

-   -   Configure them selves based on considerations, set by the access        server for the network.    -   Support automatic load balancing: AP nodes avoid data congestion        hot spots.    -   Support fail over: if one node dies, nodes connected to it        automatically switch to another.    -   Is fully functional when powered up: no installation procedure        or site survey required.    -   Support software upgrades to them selves through a        communications interface    -   Support both isochronous and asynchronous application        requirements in the same network    -   Support the wireless equivalent of switches    -   Supports centralized control and is application aware based on        settings provided by the access server.

Each AP Node is implemented as a self-contained embedded system, withall algorithms resident in its operating system. The normal day-to-dayfunctioning of the AP node is based entirely on resident controlalgorithms. Upgrades are possible through a communications interfacedescribed later.

Description of System Components

There are three typical components of the system proposed. In FIG. 1:

-   -   1. The Access server (10) “manages” the network, by setting        control parameters for the network    -   2. The “Root” Node (20), is connected to the Access Server        through an Ethernet link    -   3. Wireless communication Devices such as AP nodes (30) that        connect to the Root or other AP nodes devices to form a        communications path terminating at an Ethernet link.        To enable voice and data types of requirements to be serviced        satisfactorily within the same network configuration, the access        server (10) maintains a list of applications and their latency        and throughput requirements. Based on those application        “profiles”, the access server (10) sets the Network “profile”        that each wireless communication device (30) in the network        strives towards.

In one implementation of the invention, the root node (20) acts as theinterface between the wireless communication devices (30) and theEthernet. All wireless devices (30) communicate to the Ethernet througha root node (20), which is has a radio interface and an Ethernet link.

In that implementation of the invention, other wireless communicationsdevices or AP nodes (30) have two radios: one to communicate with itsclients which includes wireless devices such as laptops, VOIP wirelessphones etc. Clients to one AP node (30) also include other AP nodes (30)connecting to it. In FIG. 4B, for example, Node 002 has nodes 005connected to it. In addition all nodes (30) may have localclients—laptops etc.—connected to them directly. Each node, therefore,is dual purpose: It acts as a relay node—relaying traffic from a (child)node and also acts as an Access Point to local devices. In thisimplementation of the invention, redundancy is assured because of themesh characteristic of the network.

In an alternate implementation of the invention, all Wireless AP Nodesare roots—they are all wired. In this case the network is not a meshnetwork and redundancy is dependant on having a large number of nearbyAP nodes. However there is still communication between the nodes so loadbalancing and wireless switching, as described later, is supported.

Description of Variables Used by Algorithm

FIGS. 4A, 4B, describe the variables used by the algorithms. Withreference to FIG. 4A, some relevant variables are:

-   -   NI: Node Index: the unique Identifier for each wireless        communication node    -   PN: Parent Node Index: Node Index for a node connects to in        order to transmit data, For example, In FIG. 4B, the parent of        Node 005 is 002.    -   RP: Route Path. In FIG. 4B, the route path for 005 is 000 002        005, its connection route.    -   NP: Number of parents available: Each Node has choices regarding        which parent it selects to connect to, based on        latency/throughput requirements. In FIG. 4B, Node 005 can        connect to 004, 001, 002, and 000 (the root), all of whom are        within the wireless range.    -   NR: Number of unique paths some of the parents may be children        of another node in the list of NP. NR is therefore the list of        unique roots the node has. In FIG. 4B, 001 is a child of 005.        The number of unique roots is therefore 3.    -   AC: Number of Active children. This is the number of immediate        children a node has: In FIG. 4B, Node 005 has 3 children: 005,        001, 008    -   ND: Number of Descendants: Child nodes may also have their own        children. ND is the total number of descendants that are        connected, directly or indirectly to the parent node.    -   CO: Cost to connect. When a node is congested it raises the cost        of connectivity for a prospective child, thereby dissuading new        connections till the congestion is alleviated. The cost to        connect is a number transmitted to the child node. It is related        to the level of congestion    -   LR: Load on Route, is the total communications traffic (load)        from all children and their descendants that a Node sees. Thus        in FIG. 4B, the LR for Root Node 000 will be all nodes connected        to it, directly on indirectly. This information is needed to        determine if a particular parent can support the communication        needs (load) of a new child node    -   CC Load Capacity is the amount of communication traffic or load        that the node is capable of handling. It will be driven by the        buffer size of the node—since data from a node's children is        typically buffered before being retransmitted to the node's        parent.    -   LC Load from Children: This is the sum total of all traffic        being passed on from children to their parent for retransmission        to the parent's parent. In FIG. 4B Node 002 has load from        children 005, 001, 008. Each of those nodes may have children        with their loads.    -   LL Local Load: In addition to servicing children, each node may        also be servicing individual clients that comprise their local        load. For example, Node 002 services three child nodes. But in        addition, there could be laptops in its vicinity that connect to        it directly. They constitute a local load.    -   LS Local Signal Strength: is related to signal strength that a        node sees from its parent. It is actually the throughput        expected based on the actual signal strength seen by the radio        and is computed based on a look up table as shown in FIGS. 1 and        2.    -   GT Global Throughput: This is the product of all throughputs        each node along the route provides. Nodes connected to the root        have a throughput related to LS. Thus the throughput of Node 002        in FIG. 4B is 79%, based on the throughput table shown in FIGS.        1 and 2 and its distance from the root.

The Global Throughput of Node 005 is (LS to Node 002)*(GT of Node002)=0.79*70=0.55 Description of Parent Selection Algorithm

Since there is no central point of control in a distributed system, thesame algorithms, running in every node, must determine what is best,based on the information it received from the Access Server and othernearby nodes. Much of this relates to selecting correct “route” or pathto the root node. As an illustration, in FIG. 4B the route for Node 005is 000-002-005. The Node 002 is, the parent for Node 005 while Node 000is the parent for 002.

Assuming for the present, that the Access Server wishes the network tohave the maximum throughput. Then, if each node independently makes thebest selection of its parent—to maximize throughput—then a questionarises of whether one can be assured that the network as a whole isrunning as “best” as possible.

To answer this, consider the network in FIG. 4B. If the parameter set bythe Access Server is to maximize throughput. Node 002 would examine allnodes it can connect to and choose a parent that ensures the highestglobal throughput (GT).

Since GT is product function of the LS times the GT of the potentialnode. Node 005 would have examined all potential parent nodes beforeselecting node 002. Similarly Node 002 has chosen Node 000. Other nodeswould yield a lower GT. Thus, since each node is making a parentselection based on the “best” throughput, the throughput of the networkas a whole is also maximized.

Thus, each node, starting from those closest to the root and spreadingoutwards maximizes its GT based on products related to the GT at eachprevious node, the overall throughput of the system is the sum of allindividual throughputs, which have been maximized by the selectionalgorithm.

The implementation steps taken by the selection algorithm are:

1. Seek out and list all active nearby nodes.2. Remove descendants: nodes that are connected to it, or children ofnodes connected to it.3. Order the list: push nodes closer to the route (shorter routingpaths) up in the list.4. Compute total throughput for each routing in the list of connectionnodes5. Select the node that provides the best latency or max throughput orcombination of both.6. Repeat steps 1,5 on a periodic basis.To compute throughput, Nodes receive the following pieces of informationfrom all nearby nodes:1) The current routing path of the node. This is a list of nodes thatthe AP node has to connect to, in order to reach a root node. In FIG.4B, Node 005 has a routing path of 000-002-005.2) The current throughput of that node. The signal strength of thatnode's parent as seen by the node, is correlated to a look up table thatmaps the signal strength to throughput. For Node 002 (FIG. 4B), that isshown as 79.

Based on these two pieces of information, collected for all nearby APnodes, the node selects one parent node that satisfies the requirementsof low latency or high throughput. If low latency is the issue then ashort routing path is desirable: In FIG. 5 Node 005 chooses the rootnode 000. Note that the overall throughput for Node 005 is low—17%because of the large distance away from the root node, resulting in poorsignal strength. Conversely, if high throughput were required, Node 005would connect to Node 002. The overall throughput for that connectionis:

Throughput (Node 002-Node 000) * Throughput (Node 005-Node 002): 0.79 *0.70 = .55.

At the end of step 4, the global throughput—computed as a product of thelocal signal strength to that potential parent node and the GT of thepotential parent node—is computed and compared for each potential parentin the list of nearby nodes. The one with the highest throughput wins.

Controlling Network Latency/Throughput

The section on the selection of the parent assumed that only maximizingthroughput was the sole objective. Often there is a tradeoff between lowlatency and high throughput as evidenced in FIGS. 1, 2, and 3. Sinceeach AP node is in all likelihood supporting multiple applications withdiverse needs, the access server setting is probably not either extremebut something between low latency and max throughput.

The aforementioned describes the algorithm for maximized throughput. Forlowest latency, the choice of parent is restricted to the parent withthe highest throughput with an upper bound on the number of hops theparent is away from the root. There are two ways in which the AccessServer can control the latency of the network:

-   1. Place an upper bound on the number of hops admissible for any    node—this forces nodes on the fringe of the network to choose    shorter path routes. This approach acts a cut off: it forces nodes    at the fringe of the network towards selecting low latency routes,    regardless of the loss in throughput. In terms of the routing    algorithm, this translates to computing the throughput for selecting    a parent with the highest throughput that fall in a group with    latency better or equal to the upper bound.-   2. Define a latency loss threshold whereby selecting a longer route    path requires throughput gain to more than offset the loss of    latency:

Throughput(Longer Route)+Latency_loss_threshold>Throughput(Shorterroute)

If the latency loss threshold is set high, the choices a node inselecting its parent is restricted, to nodes closer to the root, withshorter route paths. In contrast to the cutoff approach this approach ismore forgiving: Selecting a longer path is allowed if throughput gainsin choosing a longer routing path offset increased latency. In terms ofthe routing algorithm, this translates to computing the throughput forall nearby nodes.

Reference is now made to re-examining the parent selection process withlatency restrictions in place. With reference to FIG. 4B, if the latencyrestriction of the first type is enforced, and the maximum number ofhops allowed for any node is set to 1, then the list of accessible nodesis reduced to node 000. Alternatively, or in conjunction with thisrestriction, if the latency threshold is set high, then even if othernodes are acceptable potential parents, the latency threshold willreduce their attractiveness and again Node 000 could be selected.

Combinations of both restrictions, based on the parameters set, resultin networks that address both latency and throughput requirements. Thiswas shown in FIG. 3, where the overall network configuration wassomewhat mid way the two extremes. It is further evidenced in FIG. 6,where increasing the latency loss threshold results in progressivelylower latency network configurations. Nodes circled in each snap shot ofthe simulation show movement towards shorter routing paths as thelatency cost factor is progressively increased.

Automatic Load Balancing

Described thus far is how the parent selection process takes intoaccount latency/throughput criteria set by the access server. However,one must also take into account how the system behaves under load, whenthe load increases at one node, causing congestion.

Since this is a distributed system, each node is responsible forselecting a parent that can service it satisfactorily—it is not part ofa congested route. During the selection process, the connect costassociated with selecting a new parent is supplied by the parent. Thus acongested route will have a higher connect cost than a less congestedroute—and a lower throughput. The routing algorithm selects the parentwith the highest throughput. As its connect cost increases a congestedparent is increasing less attractive.

In FIG. 7, the cost of connectivity is raised from 0 to 3, resulting inone node “leaving” in favor of a better route. As the cost ofconnectivity is progressively increased, more nodes select alternatepaths as evidenced in FIG. 8.

Increasing the cost of connectivity acts as an incentive for nodes tofind other routes, but does not prevent a child node from continuing itsassociation. This ensures that all child nodes are serviced always.Additionally, the cost of connectivity is increased only until all childnodes that have the option to leave have left—for example, in FIG. 7,the cost in increased only while child nodes that have multiplealternate route are still attached to a congested parent node. In otherwords, there is no reason to increase the connectivity cost for childnodes that have no alternate route and are “stuck” with the currentcongested parent node.

As the load is balanced, the congestion is reduced and the cost ofconnectivity is gradually reduced, enabling the child nodes that left toreturn. If this is not done, then nodes leaving one parent wouldcontribute to congestion elsewhere, resulting in increased cost ofconnectivity elsewhere and system instability.

One characteristic of a mesh network is the ability for nodes to selectalternate routes, in case one node fails or becomes congested. As shownin FIG. 7, each node, when computing the throughput of nearby nodes,also infers from the routing path for those nodes, how many alternatepaths are available to it. This is done by examining the routing pathsof each nearby node and classifying them based on the node that connectsto the root.

In FIG. 7, Nodes 002 and 004 connect to the root—as do other nodes shownin one embodiment with a color indicated square on their top left corner(40), for instance this may be a blue square. Node 005, therefore, canhave two routing paths: 000-002-005 and 000-004-005. Other nodes mayalso have multiple paths, but may not find a parent that, if it acceptsthem as child, has the capacity to service them, based on their currentload. Hence, some of the nodes show one parent and one route, in anothercolor indicator, for instance red (50).

It is desirable to configure the network to ensure all nodes havealternate paths. This is achieved by increasing the number of nodesconnecting to the root. The access server can force this by increasingthe latency cost factor, resulting in nodes that can connect to the rootdirectly to do so rather than through another node closer to them to theroot. This was described earlier as depicted in FIGS. 4, 5, and 6. Itcan also force some nodes to work in either low latency or highthroughput modes—in FIG. 7, the check box for each node (checked in eachcase) depicts that each node is currently in high throughput mode,working within the constraints set by the access server.

By controlling the latency cost factor, or the upper bound of the maxhops, the access server can change the configuration of the networkresulting in a higher redundancy of the system and less likelihood ofload congestion hot spots.

Real World Constraints to Load Balancing Algorithm

Implementation of the load balancing algorithm on the wireless devices,required modifications to the connect cost algorithms based on realworld constraints. Wireless devices communicating with devices runningthe load balancing software may not all be running the same software. Asan example, consider the case where the load balancing software isloaded on wireless Access Points but not on laptops communicating withthe access points. Clearly, the laptop has no way of knowing that theconnect cost has increased and therefore will continue to “stick” to theaccess point.

The load balancing algorithm has therefore been modified to work wherethere is no communication regarding connect cost by the followingapproach: When the load exceeds a cut off threshold, the Access Point(or other wireless device performing load balancing) will drop itssignal strength to the lowest possible—thereby dissuading clients suchas laptops from associating with it and encouraging them to seek anotherassociation with a higher signal strength access point.

Since the laptops seek the access point with the highest signalstrength, this is a necessary but not sufficient cause for are-association: some laptops may continue to “stick” to the accesspoint, due to proximity or a sticky algorithm. The Access Point musttherefore forcibly disassociate the laptop.

After disassociating all the stations that it needed to, in order toshed load, the access point can gradually increase its signal strengthto attract back some the stations that it disassociated. Those that didnot find other associations, will return, almost immediately afterassociation, and the access point takes them back because despite thelowered signal strength, these devices have no place to go.

This load balancing algorithm has been implemented and demonstrated toshed load by moving one laptop from one root node to another whenoverloaded by two laptops on the same root node.

Monitoring the Health of the Network

By controlling the latency cost factor, or the upper bound of the maxhops, the access server can change the configuration of the networkresulting in a higher redundancy of the system and less likelihood ofload congestion hot spots. In FIG. 7, Node 002 is shown congested andavailable “parents” that will accept it are reduced to 1 (shown in red(50)). As a result the redundancy and fail safe nature of the wirelessnetwork has been adversely affected.

Congestion in FIG. 7 is being caused by the fact that there is only oneroot node transporting all wireless traffic to the wired network.Clearly, another root node would improve the fail safe nature of thenetwork.

Algorithms have been implemented that reside in the device andperiodically check to see what potential associations are possible. Ifthe number is reduced to one then a warning (shown in red) is forwardedto the Network Management System. The system administrator is thenadvised to add another root node to preserve the fail safe nature of thenetwork. Note that this type of warning is coming from the edge deviceto the management system and without the algorithm in place, themanagement system would not know that a problem existed

Dynamic Channel Allocation and RF Interference Control

Managing the throughput of voice, video and data traffic in a wirelessnetwork is complicated by the nature of the wireless medium. Sincewireless is a shared medium, all traffic can potentially interfere withother traffic on the same radio channel—at any point in time only onedevice can be active on any one given channel. This limits the bandwidthin cases where high bandwidth traffic (e.g. video) needs to betransported or when there are many devices on the network.

One solution is to allocate different channels to devices communicatingon different portions of the network. For example, in FIG. 10, each ofthe 3 sub networks shown in Red (70), Black (80) and Blue (90) circlescan communicate on one channel internally and another channel externallythrough the parent—that is part of the other BSS.

An algorithm to define what the best channel allocations should bebetween devices and their parents has been devised and shown in FIG. 16and in FIG. 17. The algorithm sends out a beacon on all channels toinform all neighboring devices that it is intending to select a channel.All neighboring devices—that can hear the beacon—respond over the wirednetwork with the channel numbers they are using. The algorithm thentakes care to select a channel, from a list of available channels thatdoes not conflict with channels selected by neighboring devices

Dynamic Channel Allocation and Seamless Roaming Requirements

A situation can occur when, as shown in FIG. 18, some overlap betweenneighboring channels is called for. One reason for this is seamlessroaming where a laptop leaves one wireless area and enters anotheradjoining area. If the signal strengths are managed, so that the overlapbetween adjoining areas is minimized, then adjoining areas can share thesame RF channel and yet cause a reduction in bandwidth. Further, devicesmoving from one region to another will do so without having to changechannels, which is time consuming and hence disruptive.

The algorithm implemented addresses the case where siblings of a multilayered wireless network are to be assigned the same channels. In FIG.18 BSS [1,1] and

share the same channel for access by their client stations. Also BSS[2,1], [2,2][2,3] also share the same channels for seamless roaming bytheir client stations. The implementation of the algorithm thereforerequires that when one sibling makes a channel assignment, ALL adjoiningsiblings have to make the same channel assignment. The channel selectionprocess therefore requires a list of all neighboring nodes for allsiblings and their RF channels. This extension has been incorporatedinto the channel allocation algorithm. Protocols for sharing thisinformation have been implemented and tested. Appendix A heretodescribes the 802.11 Infrastructure Control Layer Protocol version 2.0.

Note that seamless roaming requires that the Wireless AP shown in FIG.18 have incorporated the features needed for switching—that is autodiscovery of new clients in its BSS. Else messages being sent to a newclient (previously connected to another BSS) will not be forwarded tothe client, now part of the another BSS. The wireless equivalent of awired switch has been implemented and is covered in a subsequentsection.

Asynchronous Application Data Flow

Algorithms that show how data from nodes will flow to the root node havebeen modeled for both high throughput and for low latency requirements.High throughput data flow requirements are discussed first.

To service asynchronous applications each node services its children ina round robin manner, ensuring that all children are serviced insequence. But to ensure that all children receive at least one servicerequest, each recently serviced child must wait for at least anotherchild to be serviced before it can be serviced again. Additionally, somechild nodes servicing applications with higher priority will be servicedbefore others.

In one implementation of this algorithm, related to this invention, thepriorities may be stored in the Access server and different applicationsfall into different priority buckets. By changing the priorities in theAccess Server, applications with higher priority are serviced beforeother competing applications with a lower priority. Also with a prioritybucket, applications with more data to transfer are serviced first.

In another implementation, the determination regarding which child toservice next is based on which child needs servicing the most. This isdetermined by examining the load of each child, each time; the nodeservices its children. It is done each time because:

-   -   Child nodes may have made other connections, if load balancing        is also active    -   Child nodes data may have changed—data with short time to live,        will have been removed

The node then makes the decision to service the child with the highestneed, in a priority bucket, provided it has not serviced that same childmost recently. This is simply to avoid any one child from “hogging” allthe attention.

This proprietary PCF (Point Control Function) implementation worked wellfor asynchronous applications, when compared to 802.11a standard DCF(Distributed Control Function) approach that was also implemented forbenchmarking reasons as shown in FIG. 9. The PCF protocol implementedper the approach described above was approximately twice as fast,largely because nodes did not have to deal with delays caused bycollision avoidance contention window delays required for wirelesstransmissions in a DCF mode.

Isochronous Application Data Flow

Isochronous applications require more deterministic service intervalsthat are less sensitive to variations of load. The algorithm describedabove is not suitable when:

-   -   Each child must be serviced by the parent in a regular and        predictable time interval    -   The amount of data transferred is relatively fixed—else it will        affect the time interval.

The algorithm to service Isochronous Application has also beenimplemented. In FIG. 10, there are two service cycles. One service cycle(70) services 3 children, the other one (80) 5 children, one of which isthe parent node of the three children in the first service cycle (70).In each service cycle, each child is visited at a regular intervaldefined by 1) the time interval between each switching from one child toanother and 2) the number of children in each service cycle.

Thus if the parent of the red service cycle (70) spends 10 ms with eachchild, it will revisit each child every 3*10=30 ms. Data from each childcannot then be retrieved at a rate faster than once every 30 ms.

Having retrieved the data, it will sit at the buffer of the parent,until the parent is serviced (the black (80) circle). Since there are 5children in that service cycle, the service period is 5*10=50 ms.

Since both service cycles are running independently of each other withno synchronization, it is impossible to predict when the parent ineither service cycle will service its children. It can be stated,however that each child in service cycle marked red (70) (005, 001, 008)will have data transferred to the root at best every 30 ms and at worstevery 50 ms. In other words, in the isochronous network, the worst timeinterval is the maximum time period of all service cycles.

If it is assumed that, to ensure multiple routing paths, there are morenodes connected to the root, then the service cycle will be driven bythe number of 1 hop nodes, in this case 5. Note that the networkconfiguration is set for high throughput. In this configuration theworst service cycle is 5T. Ironically, the network configuration for a“low latency”. Isochronous network would have been 9T, will all nodesconnected to the root. In other words, in the case of isochronousnetworks, the algorithm proposed provides a better service cycle and abetter throughput. In general, splitting the number of nodes into two ormore service cycles will improve the service cycle. The high throughputmode setting for the routing algorithm makes that happen naturally.

Internal Traffic Flow/Wireless Equivalent of Switching

Referring again to FIG. 10, Nodes 005, 001, 008 are children of Node002. The red oval (70) indicates that these form a “family”. In thetechnical parlance of 802.11, they are a “Basic Service Set” (BSS).Likewise the nodes in the black circle (80) form another family or BSS.

Traffic from Node 005 to Node 001 would logically travel to Parent 002and then from 002 to 001. This affects the throughput of the entiresystem because the traffic is buffered in 002 and then retransmitted. Ifnode 005 was aware of its siblings, within its wireless range, then Node005 and Node 001 could communicate directly over wireless. In effectthis would be a wireless equivalent of Ethernet switches.

This direct communication link between siblings (within range) increasesthroughput 100%. This is so because 2 transfers of data (Source node toparent and then Parent to destination node) are now reduced to 1transfer (Source node to destination node).

In this embodiment, the algorithm has been implemented whereby trafficintended for a destination node is automatically sent to the destinationnode if it is within the family/BSS and within range. When the routingalgorithm runs, the routing paths of each nearby node is recorded todetermine the number of hops it is away from the root. From the routingpaths, the list of siblings within range can be inferred—they all sharethe same parent in their routing paths. If data intended for thesesiblings is received, it will automatically be sent to the sibling,without involving the parent.

If the destination node is not in the list of nearby siblings, then thedata has to be sent onwards to the parent node. At that point the parentnode, which “knows” its siblings, can route traffic to one if itssiblings. Thus the switching algorithm ensures that traffic is routedonly as far as a parent whose child is a destination node.

Extensions with Multiple Radios

As shown in FIG. 10, there are at least two families or BSS in thenetwork. Node 002 belongs to both—as a parent of one (the red oval BSS)(70) and as a child to another (the black circle BSS) (80). Tocommunicate with its children, Node 002 has one radio, and another tocommunicate with its parent. There is preferably therefore, at theminimum, two radios in each node for the system to work—One radio tosupport the inward interface (the children, red oval (70)) and one tosupport the outward interface (the parent, black circle (80)).

Adding more radios to the outward interface increases throughput butalso enables more freedom in making choices related tolatency/throughput tradeoffs. For example, suppose that some trafficrequires high throughput and other traffic low latency. If thecompromise approach described in this invention is unacceptable becausethe range of requirements are too high, then two radios for the outwardinterface can reduce the range of requirements: One radio will addressmore of the low latency traffic with a low latency traffic route whilethe other will address the high throughput needs with a different highthroughput traffic route. The wireless node now begins to resemble awireless equivalent of network routers.

The algorithms described in this invention are still applicable: onlythe range of applicability has changed. The embodiment of the presentinvention is also relevant for wireless routers.

Security of the Network

Wireless transmissions are inherently insecure. While the technology toencrypt/decrypt secure data exists, the problem is communication of thekeys over wireless to the nodes, from the access server. This common keydistribution problem is addressed by the following embodiment of thesystem.

The wireless communication devices will have, as part of the algorithmsresident in their operating system, the ability to generate a public andprivate key based on the RSA algorithm. These keys will be based on someunique identifier in the AP node—the processor Chip Serial Number as anexample.

When the Wireless device is first deployed, it will be connected viaEthernet cable to the access server and the node's public key will betransmitted to the Access Server. This public key will be used by theAccess Server to transmit a common private key (using the symmetric AESencryption algorithm) to all nodes. Since only the Access Server knowsthe public key for each node, only the access server will be able totransmit this private key. Further, since the common private key for allnodes was transmitted in encrypted form to all nodes, it is impossibleto decipher the common private key without knowing the public key forthe node it was intended for. In addition, even if that public key forthat node is known, it is useless since the private key for that nodewas never exchanged. The transmission of the common Private Key is thussecure.

Secure data transmitted by one AP node will then be encrypted with thecommon private key and be decrypted only at a destination AP node. Bythe same token, all data from the Ethernet to an AP node will beencrypted with the same private key for onward transmission.

The enterprise Access Server can be used to generate a new private keyat regular intervals and transmit it to all Wireless AP Nodes in thesystem. The system is thus doubly secure.

Implementation of Algorithm in Firmware

The control algorithms described above require significantresources—CPU. Memory—resulting in large footprint applications.Wireless devices and other network aware communication devices aretypically embedded systems with low foot print requirements. A challengethat must be addressed—if this technology is to have practicalapplications is how to reduce the footprint of the software controllayer to fit into embedded devices with 64 KB or 128 KB RAM.

A self-standing executable containing some of the algorithms and supportfunctions has been produced within a footprint of less than 100 KBrunning on an Intel PXA250 Processor. Additionally in an embodiment ofthe present invention, the mesh and load balancing algorithms have beensuccessfully ported to run on the hardware depicted in FIG. 15, with afootprint of 64 KB.

The reason for the small footprint is that the approach of theembodiment of the present invention to building software is to includeonly the portions of an operating system needed by the programs. Thus,only functional blocks needed by the algorithms are added to the makefile needed by the compiler to create the executable. If stringfunctions are not required by any procedures in the program, then thestring function library is not included when the executable is made. Incontrast, a typical embedded operating system is more general purpose,requiring a larger footprint and possibly more CPU resources.

The language in which the algorithms are written is currently Java, andwill may also include Microsoft™ .NET languages. In the embodiment ofthe present invention, a Java Class file converter has been built thattakes Java Byte Code and disassembles it to produce what is referred tointernally as R (for Real) classes. R classes are the C code equivalentof the Java Op codes, used by a Java Virtual Machine (JVM) to run theJava program. The R classes map to C code which is produced afterexamining the functional libraries needed and adding them to the list ofsupport libraries needed to make an executable self standing. Once thatis completed a self-standing executable is made for the processor.

An extensible service library of software components needed to build acomplete Operating system (OS) is implemented through the embodiment ofthe present invention. This component based approach to building anOperating system from scratch enables one to select only the essentialservices needed by an application when it is ported from high levellanguages to small footprint embedded devices. Since only the essentialservices and not an entire general purpose OS is included, the footprintis compact. Additionally, there is a significant (3×-6×) performanceimprovement because layers of software needed to run code written inhigh level languages like Java or .NET languages are no longer needed.

FIG. 13 depicts how the approach of the embodiment of the presentinvention to lean code generation differs from more traditionalapproaches. On the left of the figure are the layers or stacks needed torun Java (JVM on top of linux) or C (on top of a custom RTOS OS likeVxWorks). On the right is an embodiment where Java code is examined atthe Byte code level to identify essential services. These services areadded when the Java Byte code is converted to C—by mapping Java Op codesintended for a JVM to code blocks from a library of C equivalent codeblocks. For C code, this means simply adding the OS service code blocksneeded for the C program to run without an RTOS below it. In both cases,as depicted in FIG. 13, A monolith block C code is produced, much like acompiler would, except that all the services are statically bound atdevelopment time and no dynamic bindings are required at run time, as isthe case with the layered approach on the left.

Thus there is a clear migration strategy in place from high level codegeneration to low level object code that includes all the functionalityprovided by an operating system to ensure that the object code is selfcontained.

There is implemented one version of this migration strategy where onebegins with high level code written and tested in a developmentenvironment and can swiftly migrate it to a low footprint executable.FIG. 14, depicts an extension to IBM's open extensible framework fordevelopment, called Eclipse (www.eclipse.org). As shown, there is builta framework on top of eclipse that enables one to model processes as aflow graph, where at each node of the flow graph one can insert codethat needs to be tested. The framework is also used to do a performanceanalysis of code blocks at each node to determine where the bottlenecksare. At the end of the debug and analysis cycle, Java Byte Code ispassed on to the Byte code converter (shown in FIG. 13) for lowfootprint code generation.

Since there is no OS, there is no easy way to tamper with the system.This approach—internally referred to as Application Specific Embedded OSsoftware—thereby protects the security of the algorithms and enables thealgorithms to run on low power (and less expensive) processors and withlower (and less expensive) memory requirements.

The simulations depicted are running the same code in each node shown inthe figures. The code running in each node has been compiled to objectcode for the Intel™ PX250 processor as a proof of concept (FIG. 12). Ithas also been compiled to run on a embedded system development platformshown in FIG. 15.

Upgrade Path for New Algorithms

A distributed network poses problems related to upgrading the softwareat each node. If the software is cast in concrete—as in an ASICimplementation—then there is no upgrade path available. Since thewireless standards are evolving, this is not a practical approach.

In the description of the embodiment of the modular approach togenerating a self standing executable, it becomes apparent that there isno migration path available to the system to upgrade the executableeasy.

This is resolved by providing a simple communication protocol foruploading new object code into the system. This has also beenimplemented as is internally called Simple Upgrade Protocol (SUP).

When the executable is made, a simple communication protocol is added,which, with proper authentication, using the public key of the node, canbe used upload object code into the Flash memory of the device. A verythin boot kernel with the AP Node and the rest of the code is remotelyinstalled. The boot kernel contains the simple communications protocolto upload object code and the security to ensure that only the AccessServer can access the device. By building security at the boot level,one ensures that all code loaded into the system has to beauthorized—since the security code cannot be overwritten.

Throughout the description and drawings, example embodiments are givenwith reference to specific configurations. It will be appreciated bythose of ordinary skill in the art that the present invention can beembodied in other specific forms. Those of ordinary skill in the artwould be able to practice such other embodiments without undueexperimentation. The scope of the present invention, for the purpose ofthe present patent document, is not limited merely to the specificexample embodiments of the foregoing description, but rather isindicated by the appended claims. All changes that come within themeaning and range of equivalents within the claims are intended to beconsidered as being embraced within the spirit and scope of the claims.

Appendix A Exemplary 802.11 Infrastructure Control Layer ProtocolVersion 2.0 Design Considerations

Size Limitation

Since an Ethernet packet can only be a maximum of 1500 bytes the maximumlength of a UDP datagram so as to not cause IP fragmentation is 1472(1500−20 byte IP header−8 byte UDP header). Every SNIP packet consistsof a 8 byte SNIP header followed by parameters. Since all IMCP functionsuse only 1 SNIP parameter the maximum length of the parameter value istherefore 1462 (1472−8 byte SNIP header−2 byte param length).

Packet Destination

All SNIP packets (either from Access Manager or from A/P) are sent viaUDP to either the limited broadcast (FF: FF: FF: FF: FF: FF) or a chosenmulticast address (01:00:5E:XX:XX:XX).

This method ensures that all A/P's get the message and also obviates theneed for assigning I/P addresses to every A/P.

DEFINITIONS Network Health Monitoring

Health Index

The number of unique Root paths determines the Health Index of an AccessPoint. In an “All-Root” network the Health Index determines the degreeof redundancy of the Access Point's location. In an infrastructure meshnetwork for Relay nodes the Health Index determines the number of uniquepaths to the “wire”.

Heartbeat Interval

This attribute specifies the interval at which Access Points send in a“heartbeat” signal, that specifies the conditions of their operations.

Location Awareness Scan Interval

This attribute specifies the interval, at which Access Points scan the“airspace” to know about other Access Points in their vicinity, todetermine their Health Index and for Dynamic Load Balancing purposes.

Dynamic Load Balancing

This Boolean attribute, determines whether an Access Point enables theadaptive dynamic load balancing functionality during its operation. Ifenabled the Kick-in Threshold and Delta T attributes determines the waythis functionality is put to use.

Connect Cost

The Connect Cost is a value in the range [0,3] where 0 being the minimumConnect Cost. The Access Point increases its cost of connectivity duringDynamic Load Balancing, after it determines that the load has stayedbeyond the Kick-in Threshold for a period of Delta T units. The ConnectCost is adaptively reduced according to the load.

Delta T

This attribute determines the duration of sampling for the Access Pointto determine that the load was higher or lower than the Kick-inthreshold for Dynamic Load Balancing purposes.

Kick-In Threshold

This attribute is a percentage of the total capacity of the AccessPoint. This is used in conjunction with Delta T for Dynamic LoadBalancing purposes.

Dynamic Channel Allocation

Channel Number

When Dynamic Channel Allocation is disabled, this attribute specifiesthe channel of operation for the Access Point.

Topology

Latency Throughput Control

When zero, this instructs the Access Point to always choose a Parent,whose hop count is lowest. When non-zero, the Access Point may choose to“tradeoff” latency for throughput provided the difference in throughputis more than Latency/Throughput Trade-Off attribute.

Max Allowable Hops

This attribute controls the topology of the wireless network so that,any Access Point is never more than Max Allowable Hops away from the“wire”.

Latency/Throughput Trade-Off

This attribute determines, the difference threshold for an Access Pointto choose a parent with more number of hops in exchange for lowerlatency.

PACKET INFORMATION STA Association Notification Direction AP toBroadeast/Multicast When Sent Upon STA association SNIP FUNCTION ID 3Purpose Topology Updating Param 0 Value Format

Total Size 22 Octets STA Disassociation Notification Direction AP toBroadcast/Multicast When Sent Upon STA disassociation SNIP FUNCTION ID 4Purpose Topology Updating Param 0 Value Format

Total Size 24 Octets Access Point IMCP Synchronization Direction AP toAP When Sent When an AP starts up or during scanning SNIP FUNCTION ID 11Purpose Identification, Health, Topology Param 0 Value Format

Total Size 10 Octets Access Point IMCP Acknowledgement Direction AP toAP When Sent Upon receipt of IMCP Sync SNIP FUNCTION ID 12 PurposeIdentification, Health, Topology Param 0 Value Format

Total Size 16 Octets Seek Best Parent Request Direction AP to AccessServer When Sent Upon startup and frequently SNIP FUNCTION ID 13 PurposeTopology Formation, Operation Param 0 Value Format

X = PC-1 CC = CONNECT COST (0,1,2,3 with 0 being minimum) HI = HEALTHINDEX (NUMBER OF UNIQUE ROOT PATHS) FR = FORCE REPLY CH = CHANNEL PC =PARENT COUNT (ROOT NODES SET PC TO 0) S[i] = Signal strength from Parenti N[i] = Noise from Parent i BSSID[i] = BSSID of Parent I Total Size21 + 8 * PC (MIN 21, MAX 1461) Seek Best Parent Response DirectionAccess Server to AP When Sent Upon receipt of SEEK request SNIP FUNCTIONID 14 Purpose Topology Formation, Operation Param 0 Value Format

ENC = ENCRYPTION TYPE AUT = AUTHENTICATION TYPE ESL = ESSID LENGTH KI =WEP KEY INDEX SKL = SHARED KEY LENGTH NASL = NAS ID LENGTH PRIMARYRADIUS = PRIMARY 802.1X RADIUS SERVER IP ADDRESS AUTPRT = AUTHENTICATIONPORT SECONDARY RADIUS = SECONDARY 802.1X RADIUS SERVER IP ADDRESS ACCPRT= ACCOUNTING PORT DLB = DYNAMIC LOAD BALANCING DCA = DYNAMIC CHANNELALLOCATION LTC = LATENCY THROUGHPUT CONTROL (0 for Lowest Latency, 1 forTradeoff) MAH = MAX ALLOWABLE HOPS HBI = HEARTBEAT INTERVAL LAS =LOCATION AWARENESS SCAN INTERVAL CH = CHANNEL NUMBER DT = DELTA T KTH =DYNAMIC LOAD BALANCING KICKIN THRESHOLD TO = LATENCY/THROUGHPUT TRADEOFFChannel Scan Lock Request Direction AP to Access Server When Sent Beforescanning SNIP FUNCTION ID 15 Purpose Health Param 0 Value Format

Total Size 16 Octets Channel Scan Lock Response Direction Access Serverto AP When Sent When Access Server determines SNIP FUNCTION ID 16Purpose Health Param 0 Value Format

Total Size 16 Octets Channel Scan Release Lock Request Direction AP toAccess Server When Sent After finishing scanning SNIP FUNCTION ID 17Purpose Health Param 0 Value Format

Total Size 16 Octets Channel Scan Release Lock Response Direction AccessServer to AP When Sent Upon receipt of Lock Release Request SNIPFUNCTION ID 18 Purpose Health Param 0 Value Format

Total Size 16 Octets Access Point Reset Direction Access Server to APWhen Sent When settings change SNIP FUNCTION ID 19 Purpose OperationParam 0 Value Format

Total Size 16 Octets

REFERENCES

-   -   1. Simple Network Information Protocol SNIP, Sriram Dayanandan,        January 2003.

1. A dynamic wireless network comprising: a parent node; a plurality ofchild nodes coupled to the parent node; wherein at least some of thechild nodes are sibling nodes that are configured to directlycommunicate with each other.